| Lecture |
Topics |
Complementary
Readings |
Assigned
Readings |
| Jan 7 |
Course overview |
||
| Jan 12 |
basics of probabilities and
statistics |
||
| Jan 14 |
Bayesian networks, exact
inference |
||
| Jan 19 |
lecture canceled |
||
| Jan 21 |
lecture canceled |
||
| Jan 26 |
approximate inference (Monte
Carlo techniques) |
GCSR Chapt 11, RN Sect 14.5, An
Introduction to Monte Carlo Methods |
An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network, Loopy belief propagation for approximate inference: An empirical study |
| Jan 28 |
statistical learning (Bayesian
learning, maximum likelihood, maximum a posteriori hypothesis) |
RN Chapt 20 |
|
| Feb 2 |
single parameter models,
conjugate priors |
A tutorial on learning with Bayesian networks | |
| Feb 4 |
informative/non-informative
priors |
||
| Feb 9 |
multi-parameter models |
Bernardinelli, Clayton and
Montomoli, Bayesian estimates of disease maps: how important are
priors? Statis in Medicine 14, 2411--2431 (not available online, get it
from the library) |
|
| Feb 11 |
hierarchical models |
||
| Feb 16 |
reading break |
|
|
| Feb 18 |
reading break |
||
| Feb 23 |
Mixture models, Bayesian
clustering |
Piotr Gmytrasiewicz and Prashant Doshi, "A Framework for Sequential Planning in Multiagent Settings", in Journal of AI Research (JAIR), Vol 24: 49-79, 2005 | |
| Feb 25 |
Dirichlet process (aka the
Chinese restaurant process) |
||
| Mar 2 |
Hierarchical dirichlet process
(aka the Chinese restaurant franchise) |
Latent
dirichlet allocation - ►stanford.edu
[PDF] DM Blei, AY Ng, MI Jordan - The Journal of Machine Learning Research, 2003 |
|
| Mar 4 |
Hierarchical dirichlet process (aka the Chinese restaurant franchise) | ||
| Mar 9 |
Pitman-Yor Process |
Hierarchical Dirichlet Processes |
|
| Mar 11 |
Beta Process (aka the Indian
buffet process) |
||
| Mar 16 |
Gaussian Process | Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics in Practice, Cambridge, UK: Cambridge University Press, to appear. | |
| Mar 18 |
Regression with Gaussian Processes | ||
| Mar 23 |
Classification with Gaussian Processes | Y. Engel, P. Szabo, and D. Volkinshtein. Learning to control an octopus arm with Gaussian process temporal difference methods. In Yair Weiss, Bernhard Schölkopf, and John C. Platt, editors, Advances in Neural Information Processing Systems 18, pages 347-354, Cambridge, MA, U.S.A., 2006. The MIT Press. | |
| Mar 25 |
Classification with Gaussian Processes | ||
| Mar 30 |
Covariance functions for Gaussian Processes | A. Kapoor, K. Grauman, R. Urtasun, and T. Darell. Active learning with Gaussian processes for object categorization. In Proceedings of the International Conference in Cmputer Vision, 2007. | |
| Apr 1 |
Model selection for Gaussian Processes | ||
| Apr 6 |
Relation between Gaussian Processes and other approaches (e.g., support vector machines) | P. Sollich. Bayesian methods for support vector machines: Evidence and predictive class probabilities. Machine Learning, 46(1-3):21-52, 2002. | |
| Apr 8 |
Relation between Gaussian Processes and other approaches (e.g., support vector machines) |